Music Data Analysis

This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.

“" . . . what makes this book unique is that it covers a much broader range of topics. Not only does it present a concrete tutorial on signal processing and music information retrieval . . . , but it also talks about interesting topics such as emotions, automatic composition, hardware, and others, so readers are sure to find novel information . . . In summary, MusicDataAnalysis is well thought-out and well written. It chooses to emphasize a breadth of topics rather than specialize in specific ones. This book nicely accomplishes its goal of serving as an introductory textbook for music research. It is also a very useful reference and valuable resource for individuals seeking new directions in the field." ~Yupeng Gu, Journal of the American Statistical Association ". . . the book is impressive in its structure, comprehensiveness, clarity and accuracy. . . This text has staked out a specialised interdisciplinary niche, but as a self-contained guide to computational methods for music, I think it unlikely to be surpassed in the near future." ~ David Bulger, Australian & New Zealand Journal of Statistics "Theoretical and practical exercises based on R and MATLAB are provided in the book's web site, as well as example data sets. The book is very clearly written, and the style is fairly uniform despite the large number of authors. In sum, a very useful and enjoyable book."~Ricardo Maronna, Stat Papers contained guide to computational methods for music, I think it unlikely to be surpassed in the near future." ~ David Bulger, Australian & New Zealand Journal of Statistics "Theoretical and practical exercises based on R and MATLAB are provided in the book's web site, as well as example data sets. The book is very clearly written, and the style is fairly uniform despite the large number of authors. In sum, a very useful and enjoyable book."~Ricardo Maronna, Stat Papers”

" . . . what makes this book unique is that it covers a much broader range of topics. Not only does it present a concrete tutorial on signal processing and music information retrieval . . . , but it also talks about interesting topics such as emotions, automatic composition, hardware, and others, so readers are sure to find novel information . . . In summary, MusicDataAnalysis is well thought-out and well written. It chooses to emphasize a breadth of topics rather than specialize in specific ones. This book nicely accomplishes its goal of serving as an introductory textbook for music research. It is also a very useful reference and valuable resource for individuals seeking new directions in the field." ~Yupeng Gu, Journal of the American Statistical Association

". . . the book is impressive in its structure, comprehensiveness, clarity and accuracy. . . This text has staked out a specialised interdisciplinary niche, but as a self-contained guide to computational methods for music, I think it unlikely to be surpassed in the near future." ~David Bulger, Australian & New Zealand Journal of Statistics

"Theoretical and practical exercises based on R and MATLAB are provided in the book’s web site, as well as example data sets. The book is very clearly written, and the style is fairly uniform despite the large number of authors. In sum, a very useful and enjoyable book." ~Ricardo Maronna, Stat Papers

" . . . what makes this book unique is that it covers a much broader range of topics. Not only does it present a concrete tutorial on signal processing and music information retrieval . . . , but it also talks about interesting topics such as emotions, automatic composition, hardware, and others, so readers are sure to find novel information . . . In summary, MusicDataAnalysis is well thought-out and well written. It chooses to emphasize a breadth of topics rather than specialize in specific ones. This book nicely accomplishes its goal of serving as an introductory textbook for music research. It is also a very useful reference and valuable resource for individuals seeking new directions in the field." ~Yupeng Gu, Journal of the American Statistical Association

". . . the book is impressive in its structure, comprehensiveness, clarity and accuracy. . . This text has staked out a specialised interdisciplinary niche, but as a self-contained guide to computational methods for music, I think it unlikely to be surpassed in the near future." ~David Bulger, Australian & New Zealand Journal of Statistics

"Theoretical and practical exercises based on R and MATLAB are provided in the book’s web site, as well as example data sets. The book is very clearly written, and the style is fairly uniform despite the large number of authors. In sum, a very useful and enjoyable book." ~Ricardo Maronna, Stat Papers

Dietmar Jannach, Günter Rudolphm and Igor Vatolkin are affiliated with the Department of Computer Science, TU Dortmund University, GermanyClaus Weihs is affiliated with the Department of Statistics at TU Dortmund University, Germany

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